Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/equinor/neqsim/reviewgit clone --depth 1 https://github.com/equinor/neqsimWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00119 | $0.01662 |
| Opus 5 | $0.00060 | $0.00831 |
| Sonnet 5 | $0.00024 | $0.00332 |
| Haiku 4.5 | $0.00012 | $0.00166 |
Grade A, and why
review task deliverables scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Review Agent. Your job is to grade an engineering task folder before the user opens a PR. You do not modify the task; you report what is missing or inconsistent and let the user (or another agent) fix it.
When to Use
- The user says "review my task", "audit results.json", "is this ready to merge", "check this notebook", or finishes a task and asks for a quality gate.
- Before invoking
gh pr create.
Inputs
- A path to a task folder under
task_solve/(orall).
Workflow
-
Schema check.
<python-executable> devtools/validate_task_results.py <task_folder>Report errors as FAIL, warnings as WARN.
-
Consistency check.
<python-executable> devtools/consistency_checker.py <task_folder>Report any CRITICAL issues as FAIL; non-critical as WARN.
-
Capability assessment. Confirm
step1_scope_and_research/capability_assessment.mdexists, has sections 2 and 3 populated (not template placeholders), and references at least one skill. Also confirm it ends with acapability_readiness:verdict line (READY/READY_WITH_WORKAROUNDS/NEEDS_NIP/BLOCKED, seeneqsim-capability-map§L). WARN if the verdict is missing; if the verdict isNEEDS_NIPorBLOCKED, confirm a matchingneqsim_improvements.mdNIP exists — FAIL if it does not. -
Notebook execution. For each
.ipynbinstep2_analysis/:- Verify cells have
execution_countset (i.e. were actually run) - Verify each cell that produces a figure has a
dataoutput cell - WARN if any cell has empty outputs and
execution_count: null
- Verify cells have
-
Figure → discussion traceability. Read
results.json. For every entry infigure_captions, check there is a matching entry infigure_discussioncovering observation, mechanism, implication, and recommendation. -
Standards & uncertainty (Standard/Comprehensive only). Confirm
standards_applied,uncertainty, andrisk_evaluationsections are populated. The schema check in step 1 already enforces thatuncertaintypercentiles are numeric and ordered (p10 ≤ p50 ≤ p90) and that anybenchmark_validationblock is well-formed — treat those as FAIL if step 1 reported them. Also check the substance, not just the shape: at least three graded benchmark comparisons, each against a reference that outranks the model basis; and a converged Monte Carlo (sample count and median drift). The community skillsneqsim-benchmark-reference-dataandneqsim-uncertainty-quantification(equinor/neqsim-community-skills) expose those checks asblockers(); flag a task that hand-rolled the blocks and skipped them.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 138 lines · 119 tokens per session scan A d182b342e191
review task deliverables is an agent published in the GitHub repository equinor/neqsim (147 stars, last pushed 3d ago), licensed Apache-2.0. It adds 119 tokens to every session and 1,662 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
batch-processing-optimizer
Batch job and bulk operation optimization specialist.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.